HBR's 10 Must Reads 2019

(singke) #1
ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD

Despite their rapidly expanding experience with cognitive tools,
however, companies face signifi cant obstacles in development and
implementation. On the basis of our research, we’ve developed a
four-step framework for integrating AI technologies that can help
companies achieve their objectives, whether the projects are moon
shoots or business-process enhancements.



  1. Understanding the Technologies


Before embarking on an AI initiative, companies must understand
which technologies perform what types of tasks, and the strengths
and limitations of each. Rule-based expert systems and robotic
process automation, for example, are transparent in how they do
their work, but neither is capable of learning and improving. Deep
learning, on the other hand, is great at learning from large volumes
of labeled data, but it’s almost impossible to understand how it cre-
ates the models it does. This “black box” issue can be problematic in
highly regulated industries such as fi nancial services, in which reg-
ulators insist on knowing why decisions are made in a certain way.
We encountered several organizations that wasted time and
money pursuing the wrong technology for the job at hand. But if
they’re armed with a good understanding of the diff erent technol-
ogies, companies are better positioned to determine which might
best address specifi c needs, which vendors to work with, and how
quickly a system can be implemented. Acquiring this understand-
ing requires ongoing research and education, usually within IT or an
innovation group.
In particular, companies will need to leverage the capabilities of
key employees, such as data scientists, who have the statistical and
big-data skills necessary to learn the nuts and bolts of these tech-
nologies. A main success factor is your people’s willingness to learn.
Some will leap at the opportunity, while others will want to stick
with tools they’re familiar with. Strive to have a high percentage of
the former.
If you don’t have data science or analytics capabilities in-house,
you’ll probably have to build an ecosystem of external service

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